The association of meteorological parameters and AirQ+ health risk assessment of PM2.5 in Ratchaburi province, Thailand

Air quality is heavily influenced by rising pollution distribution levels which are a consequence of many artificial activities from numerous sources. This study aims to determine the relationship between meteorological data and air pollutants. The health effects of long-term PM2.5 were estimated on expected life remaining (ELR) and years of life lost (YLL) indices in Ratchaburi province during the years 2015–2019 using AirQ+ software. Values obtained from the PM2.5 averaging, and YLL data were processed for the whole population in the age range of 0–29, 30–60 and over 60. These values were entered into AirQ+ software. The mean annual concentration of PM2.5 was highly variable, with the highest concentration being 136.42 μg/m3 and the lowest being 2.33 μg/m3. The results estimated that the highest and lowest YLL in the next 10 years for all age groups would be 24,970.60 and 11,484.50 in 2017 and 2019, respectively. The number of deaths due to COPD, IHD, and stroke related to long-term exposure to ambient PM2.5 were 125, 27 and 26, respectively. The results showed that older people (> 64) had a higher YLL index than the groups aged under 64 years. The highest and lowest values for all ages were 307.15 (2015) and 159 (2017). Thus, this study demonstrated that the PM2.5 effect to all age groups, especially the the elderly people, which the policy level should be awared and fomulated the stratergies to protecting the sensitive group.

The most detrimental factors that can influence the rise of air pollutants in urban cities are emissions from human activities 1 such as power plants, industrial facilities, transportation, agriculture and waste treatment. In Thailand, there are three major sources of air pollution: vehicle emissions in the cities, biomass burning in rural and border areas,and emissions from industrial facilities 2 . Many research studies have found that there is a relationship between seasonal changes and their effects on air pollution. For example, the climate of summertime conditions contribute to the increase of particulate matter (PM 2.5 ). Heat triggers more conditioning power usage in facilities and vehicles, thereby increasing particle emission, sunlight and heat transforms and worsen air pollution by triggering reactions between atmospheric particles such as nitrogen oxide and oxygen, forming ozone and transforming primary particles into even smaller particles which are hazardous to health. Heat waves and poor air movement create stagnant atmospheric conditions where air pollutants are trapped and accumulate over the ground level. Although indirectly, seasonal variations were significant contributors to human activities and air quality 3,4 . The studied by Ji et al. also demonstrated that heat waves enhance the impact of particulate matter on circulatory motality 5 . However, the temperature inversion also the remarkable factor that can trap the pollutant lead to higher pollutant concentration 6 . Additionally, winds influenced by seasonal variation can also affect air pollution by dispersing contaminants farther away from their source, nevertheless, higher winds in dry rural areas can generate dust 7,8 . The effects of these factors are shown clearly by a study in Beijing by Chen Ratchaburi province is the one of Thailand's central provinces, and is located west of Bangkok. This province is measured to be 5196 square kilometers wide with a population over Eight Hundred Thousand in 2018. The second contains the Tenasserim mountains and forests with an elevation of about 200-300 m. The last is the central area of this province is abundant with wetlands due to the river flowing through. Ratchaburi is one of the 14 provinces in Thailand with a large industrial estate. Industry here mainly deals with electricity, natural gas (Power plant), automotive, sugar, paper, and textiles, etc. The major commercial centers are distributed in the center of the province. Moreover, Ratchaburi is facing an air pollution crisis, due to also having a similar seasonal problem to Bangkok. The correlation between the varying air pollution data and varying seasonal changes in Ratchaburi, Thailand was drawn by analyzing statisticalresults collected from over a decade. In contrast to Beijing in China, Thailand undergoes three seasons that vary over each year; a dry season in mid-Febuary to mid-May, rainy/monsoon months from mid-May to mid-October, and a dry/cooler season during mid-October until mid-Febuary 13 . In the past years, several tools, including AirQ, BenMAP, Aphekom, and AirQ+ , and various organizations were developed for evaluating the health impacts along with the assessment of air pollution 14 . WHO developed the software AirQ+ for quantifying air pollution and its effect on health both in short-term and long-term. This research aims to survey the correlation between PM 2.5 concentrations, meteorological parameters, and the estimate of all-cause annual mortality and mortality from cerebrovascular disease (stroke), ischemic heart disease (IHD), and chronic obstructive pulmonary disease (COPD) attributed to long-term exposure, and estimation of the health effects of PM2.5 on YLL indices in Ratchaburi from 2015 to 2019 using AirQ+ software.

Results
Air quality in Ratchaburi Province. The average concentration of PM 2.5 was 26.86 μg/m 3 , which exceeding the NAAQS of Thailand (50 μg/m 3 ) in some occasion, especially the dry season. However, severe amounts exceeding the WHO Air quality guideline values were also observed. The maximum 24-h average PM 2.5 concentrations were 136.42 μg/m 3 , indicating that extreme particulate matter air pollution has occured in Ratchaburi province. CO, NO 2 and SO 2 concentrations in Ratchaburi were demonstrated to be under AQI standards. Ratchaburi had the O 3 value at the minimum of 1.50 ppb, whereas the maximum concentration was 95.50 ppb. In some periods, the 8-h O 3 presented over AQI. Additionally, the meteorological factor shows the lowest average wind speed (WS) being 1.2 m/s but the largest daily maximum would be 3.0 m/s. The average of Temperature (T) was 28.2 °C, and the highest of T was 33.2 °C. The annual average of wind direction (WD) blow from 245 degrees (Table 1).  (Fig. 1).   www.nature.com/scientificreports/ ambient air of Ratchaburi province was higher than the recommended value of WHO, and its variation ranged from 2.4 to 3.0 times higher than 10 μg/m 3 .

AirQ+ mordel estimations. Chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD)
and stroke attributable cases. The AirQ+ estimation model demonstrated that people in Ratchaburi who have been exposed to long-term PM 2.5 may have died due to COPD in the period of 2017-2018 for 125 cases as the annual average number. Moreover, the association beween PM 2.5 and IHD was observed. The study found that 219 people may have died from IHD due to long-term exposure to PM 2.5 . In addition, there were 27 attributable cases per 100,000 for IHD due to PM 2.5 . Furthermore, the estimation model also demonstrated that 128 cases by an annual average may have died due to stroke-related complications due to long-term exposure to PM 2.5 . However, there were 26 attributable cases per 100,000 for stroke due to PM 2.5 (Fig. 3).

Estimation of the health effects. The
Year of Life Lost (YLL) was used as a health indicator in the development of public environmental policies 14 . Lin suggested that the essential to YLL can be used to identify appropriate interventions for risk management and improve the local efficacy for rapid response to air pollution 15 . Table 2 demonstrates the comparison between mortalies from PM 2.

Discussion
In this study, the air quality and health data from Ratchaburi province were collected as secondary data. The analysis to determining the association of PM 2.5 to metheorological parameters was performed using AirQ+ software designed by World Health Organization 16 . The annual average of PM 2.5 in Ratchaburi was 26.86 μg/m 3 , which could be observed as exceeding the standard in some period but extreamly exceeds the WHO guildline   17 . In addition to the relationship with human activity, especially the automobile derived the PM pollution 18 . In terms of gaseous pollutants, the annual average concentration were observed under the AQI. Our study showed the weak positive correlation between PM 2.5 and temperature and humidity, while a studied from China and Hong Kong showed the major meteorological factors affect the accumulation, dispersion and concentration of PM 2. 5 19,20 . From this point, it might be our sample size was small and the different in setting. The study of Vichit-Vadakan et al. showed that the gaseous pollutants i.e. nitrogen, nitric oxide and ozone were strong associated with several different mortality outcome 21 . Over the course of five years' data collected, we detected the same pattern of PM 2.5 concentration in every collected years with two peaks occurring twice a year in dry season that comply with previous studied that observed the PM 2.5 obsvious exceed the standard in dry season 22 . The studied of Figueroa-Lara et al. showed that the PM 2.5 concentration in warm-dry season were significantly higher than cold-dry season 23 14,24 . Moreover, the study site from China, Japan and South Korea also found the trend of total deaths were driven by population aging 25 . On the other hand, the study was inconsistent with Guo et al. study on the air pollution load on the YLL in Beijing, China 26 . However, when compared with studied from Middle Eastern populations, our study showed the YLL was lower dramatically 27 . Nevertheless, the different setting like in Europe, the overall air pollution that relate to morbidity and mortality have decrease considerably in the last three decades 28 . This study also showed the elderly population have high YLL value than others age group, due to the sensitive group. Consequently, the aging population might impact from medical resources and expenditures, and diseases by age group 29 .
This study was performed as a pilot study to investigating the PM 2.5 effect on YLL in the western side border of Thailand. The results of this study could potentially be beneficial as platforms for further studies in other areas, provided with valid data, hence, convincing the environmental health department on the concern of the effects of particulate matter and pollutant parameters that could pose signifcant impacts on health.

Conclusions
This study aimed to evaluate the relationships of PM 2.5 with meteorological in Ratchaburi province. The results of correlation analysis showed a weak positive correlation between PM 2.5 concentrations and average monthly temperature (r = 0.42, P < 0.05) and average monthly humidity (r = 0.37, P < 0.05) in Ratchaburi province. For this purpose, AirQ+ as the health effects of long-term exposure to PM 2.5 on YLL for 10 years. In dry period, PM 2.5 concentration was over the WHO standard amount in all studies years. The results also showed that older people (> 60) had a higher YLL index than those group aged under 60 years. The highest and lowest values for all age were 336.10 (2019) and 159.30 (2017). On the other hand, the highest and lowest rates of this index were observed in the next 10 years for all ages: 31,819.90 in 2019 and 15,394 in 2017, respectively. The finding of this research can be used for further air quality and exposure measurement for reducing mortality.
However, the core limitation of this study is small sample size; the observed trend might not represent the overall population of the west part of country. The larger sample size and multi-area study should be considered to observing and verifying the trends. This study could be platform for research to increase the study scale, leading to environmental policy, health plan evaluation, as well as the welfare plan for population.

Methods and data collection
Study site. Air monitoring stations were positioned in Ratchaburi, located at the Regional Environment Office 8th. Ratchaburi province lies in Western Thailand and its border is connected to the Tanintharyi Region of Myanmar. The total population is over Eight Hundred Thousand and the area is 5196 square kilometers (Fig. 4). This city is suffering from air pollution from several sources such as powerplants, industry, agricultural activities, and transportation. Data collection. The PM 2.5 and air quality parameters data were collected in the period of January 1st, 2015 to December 31st, 2019 from Pollution Control Department (PCD). The data was acquired at every hour from 1 am. to 11 pm. The data cleaning process was performed by excluding the invalid time and date. The health data in this study was collected in the period of 2015 to 2019 with the ICD10 code of J44 for Chronic Obstructive Pulmonary Diseses (COPD), I2 for Ischaemic Heart Disease (IHD) and I6 for stroke recorded by Ministry of Public Health, Thailand. AirQ+ software. The valid PM 2.5 and health data for over a 5 year period were entered into the AirQ+ software to calculate the YLL. The baseline incidence rate of all-cause mortalities and the averages data of PM 2.5 , Health data and YLL were converted to a .CSV file and inputs to AirQ+ software 14 . Table 3 shows a summary of inputted data to the AirQ+ software.
Statistical analysis. The descriptive statistic (mean, standard variation and varience) were used to analyze the annual air quality parameters to compare with the standard of Air Quality Index (AQI). The spearman rank correlation was performed to investigate the correlation of PM 2.5 to other air quality parameters.